set.seed(5)
canvas_flow(colors = colorPalette("retro3"), lines = 5000, lwd = 0.05,
            iterations = 1000, stepmax = 0.1, angles = NULL)

### Data Description:

identify initial network format, describe and identify the nodes (including how many nodes are in the dataset), what constitutes a tie or edge (including how many ties, whether ties are directed/undirected and weighted/binary, and how to interpret the value of the tie if any), whether or not there are edge attributes that might be used to subset data or stack multiple networks (e.g., tie type, year, etc). Not every feature of the network needs to be described, but description should orient reader to the network data and provide any necessary context for the results provided.

Provide at least two or three noteworthy results, including the relevant statistics and interpretation. For example, explaining which node(s) are most central and which are least central. Discuss (with any related evidence) whether or not the node(s) behavior is in line with or violates expectations based on the degree centrality measure. What do you make of network density and centralization measures? What about differences in the binary 100 million threshold network vs valued trade flows network. Check tutorial for other ideas of results to discuss.

data(flo, package="network")


statted_florentine<-as.network(flo)

print(statted_florentine)
##  Network attributes:
##   vertices = 16 
##   directed = TRUE 
##   hyper = FALSE 
##   loops = FALSE 
##   multiple = FALSE 
##   bipartite = FALSE 
##   total edges= 40 
##     missing edges= 0 
##     non-missing edges= 40 
## 
##  Vertex attribute names: 
##     vertex.names 
## 
## No edge attributes
plot(statted_florentine)

Nodes and Edges:

As can be seen from the graph and summary statistics above the florentine family dataset is made u

sna::dyad.census(statted_florentine)
##      Mut Asym Null
## [1,]  20    0  100
sna::triad.census(statted_florentine)
##      003 012 102 021D 021U 021C 111D 111U 030T 030C 201 120D 120U 120C 210 300
## [1,] 324   0 195    0    0    0    0    0    0    0  38    0    0    0   0   3
gtrans(statted_florentine)
## [1] 0.1914894
isolates(statted_florentine)
## [1] 12
network.density(statted_florentine)
## [1] 0.1666667
gden(statted_florentine, diag=FALSE)
## [1] 0.1666667
sna::degree(statted_florentine)
##  [1]  2  6  4  6  6  2  8  2 12  2  6  0  6  4  8  6
florentine.nodes<-data.frame(name=statted_florentine%v%"vertex.names",
                             degree=sna::degree(statted_florentine))
florentine.nodes
sna::degree(statted_florentine, cmode="indegree")
##  [1] 1 3 2 3 3 1 4 1 6 1 3 0 3 2 4 3
sna::degree(statted_florentine, cmode="outdegree")
##  [1] 1 3 2 3 3 1 4 1 6 1 3 0 3 2 4 3
florentine.nodes <- data.frame(name=statted_florentine%v%"vertex.names",
    totdegree=sna::degree(statted_florentine),
    indegree=sna::degree(statted_florentine, cmode="indegree"),
    outdegree=sna::degree(statted_florentine, cmode="outdegree"))

florentine.nodes
summary(florentine.nodes)
##      name             totdegree     indegree     outdegree  
##  Length:16          Min.   : 0   Min.   :0.0   Min.   :0.0  
##  Class :character   1st Qu.: 2   1st Qu.:1.0   1st Qu.:1.0  
##  Mode  :character   Median : 6   Median :3.0   Median :3.0  
##                     Mean   : 5   Mean   :2.5   Mean   :2.5  
##                     3rd Qu.: 6   3rd Qu.:3.0   3rd Qu.:3.0  
##                     Max.   :12   Max.   :6.0   Max.   :6.0
hist(florentine.nodes$totdegree, main="Climate Influence: In-degree Distribution", xlab="Nominations Received")

hist(florentine.nodes$indegree, main="Climate Influence: In-degree Distribution", xlab="Nominations Received")

hist(florentine.nodes$outdegree, main="Climate Influence: In-degree Distribution", xlab="Nominations Received")

florentine.nodes$outdegree
##  [1] 1 3 2 3 3 1 4 1 6 1 3 0 3 2 4 3